TocoDecoy: A New Approach to Design Unbiased Datasets for Training and Benchmarking Machine-Learning Scoring Functions

Development of accurate machine-learning-based scoring functions (MLSFs) for structure-based virtual screening against a given target requires a large unbiased dataset with structurally diverse actives and decoys. However, most datasets for the development of MLSFs were designed for traditional SFs and may suffer from hidden biases and data insufficiency. Hereby, we developed a new approach named Topology-based and Conformation-based decoys generation (TocoDecoy), which integrates two strategies to generate decoys by tweaking the actives for a specific target, to generate unbiased and expandable datasets for training and benchmarking MLSFs. For hidden bias evaluation, the performance of InteractionGraphNet (IGN) trained on the TocoDecoy, LIT-PCBA, and DUD-E-like datasets was assessed. The results illustrate that the IGN model trained on the TocoDecoy dataset is competitive with that trained on the LIT-PCBA dataset but remarkably outperforms that trained on the DUDE dataset, suggesting that the decoys in TocoDecoy are unbiased for training and benchmarking MLSFs.

Automated summary

The development of accurate machine-learning-based scoring functions for virtual screening requires unbiased and diverse datasets. However, most existing datasets may suffer from hidden biases and data insufficiency. In this study, we developed a new approach named TocoDecoy to generate unbiased and expandable datasets, and evaluated its performance compared to other datasets.